Abstract: Considering that the model is easy to overfit and cause the target misdetection and missed detection under the condition of few samples, this paper propose the few-shot object detection via the online inferential calibration (FSOIC) based on the two-stage fine-tuning approach (TFA). In this framework, a novel Attention-FPN network is designed to selectively fuse the features by modeling the dependencies between the feature channels, and direct the RPN module to extract the correct novel classes of the foreground objects in combination with the hierarchical freezing learning mechanism. At the same time, the online calibration module is constructed to encode and segment the samples, reweight the scores of multiple candidate objects, and correct misclassifying and missing objects. The experimental results in the VOC Novel Set 1 show that the proposed method improves the average nAP50 of the five tasks by 10.16% and performs better than most comparisons.